Summary
[Post rebuttal update] The authors have addressed my main concern about providing an overarching framework for their evaluation, using the Elasticity - Plasticity tradeoff. I will raise my score from 5 to 6.
This paper evaluates the influence of several model editing methods on the models' general capabilities. Their key dimensions of analysis include performance deterioration after several sequential single edits, the robustness of instruction-tuned models, the effect of LLM size and how editing mechanisms affect model safety. The paper stresses that current editing approaches are suitable only for minor updates and highlights the need for more practical and reliable editing techniques. They reveal that while a few edits have minimal impact, extensive edits can lead to significant degradation, including a phenomenon called muting effect, where models tend to produce empty outputs.
Strengths
1. The study provides a thorough assessment of various model editing methods across different language models, offering valuable insights into their effects on general capabilities.
2. By highlighting the pitfalls of current editing methods, such as knowledge distortion and catastrophic forgetting, the paper addresses critical issues that need to be resolved for model editing approaches to work in practical deployments of LLMs. The research emphasizes the impact of editing on the overall performance of language models, including world knowledge, reading comprehension, reasoning, and safety, rather than just on specific tasks. This broader perspective is crucial for understanding the real-world implications of model editing.
3. The study’s exploration of how editing affects the safety of models is particularly valuable, as it addresses a key concern for deploying language models in sensitive applications.
4. The paper claims to be the first to comprehensively evaluate and analyze the general abilities of edited language models. If this claim is true, then it makes it a timely contribution.
Weaknesses
1. The main weakness of the paper is in the exhaustive nature of the evaluation. While the paper evaluates multiple models, the findings may not generalize across all types of language models or specific applications, potentially limiting the broader applicability of the results. It is unfair to ask the authors to work with tons of LLMs but an application stratified view is missing.
2. The paper focuses more on empirical evaluation rather than providing deep technical insights into why certain methods cause performance degradation or safety issues. A more detailed technical analysis could enhance understanding and drive innovation. Note that by technical analysis, I do not mean theoretical analysis, but rather a deeper insight as to what issues are causing these failure modes. Are we not sanitizing the edits well? Are we overfitting? Is there an early stopping criterion that may work here? While pointing out flaws in existing methodologies is useful, since there are many such instances in the LLM world, well grounded research has to provide insights as to why the methodologies are flawed.
3. As an example of (2) above: The observation of the "muting effect," where models produce empty outputs after extensive edits, is concerning. However, the paper does not propose concrete insights as to why this issue might occur, leaving an important problem unaddressed.
4. There are also several different dimensions to consider for an analysis such as this: Scale of the model, prompting conditions or methodologies, whether models benefit from RAGs or not. Since the authors stress practical deployments, they should ensure their results are consistent with practical deployments as well.
Questions
In addition to points above, I wonder what the author's thoughts are on what their best insights are as to why the model performance degrades? How would a practitioner deploying a complex LLM API go about figuring this out? consider cases where they have full models access or just API access.
Limitations
In addition to addressing the weaknesses above, I encourage the authors to think of a more comprehensive evaluation framework to showcase their work. Evaluations of LLMs are themselves brittle -- hence any evaluation needs to have sufficient power to make a solid claim. In the current situation, the paper is indeed well written, but not statistically strong nor comprehensive.